Protein complexes are defining molecular entities that integrate multiple gene products to perform cellular functions. The main objective of our work is to understand the cell’s functional organization and to link individual proteins to signalling cascades and disease processes. Therefore, we have developed technologies such as an automated yeast two-hybrid (Y2H) system, which allow the efficient identification of human protein-protein interactions (PPIs). Using these methods we have generated various interaction networks for neurodegenerative diseases and signalling cascades.
Recently, we have started to integrate data from protein interaction and gene expression studies to predict tissue-specific huntingtin (HTT) interactions that are altered in Huntington’s disease (HD) brains. Applying a method termed INFIDEX (interaction network filtering by differentially expressed genes), a disease-relevant, brain-specific PPI network was created, linking 14 potentially dysregulated proteins directly or indirectly to HTT. Analysis of literature data confirmed the predictive value of this unbiased network modeling strategy. One of the identified proteins that directly associate with HTT is the neuron-specific CRMP1 (collapsin response mediator protein 1), which we predicted to be abnormally down-regulated in HD brains. In a Drosophila model of HD, overproduction of CRMP1 efficiently suppressed polyQ-mediated HTT aggregation and photoreceptor degeneration. Moreover, motor impairment and survival phenotypes were improved by CRMP1 overexpression, indicating that this protein influences both polyQ-induced protein misfolding and neurotoxicity. The results were also confirmed in cell-free and cell-based assays.
Our studies indicate that perturbed, disease-relevant PPIs are predictable by network modeling strategies. We propose that this approach is applicable to a wider range of protein misfolding and other diseases.